Near-Wall Turbulent Transport Knowledge for Suitable Flow Assurance Strategies
Bibliographic record
Abstract
Gas-liquid transportation from the deep ocean floor level to wellhead and, then, to platforms and land processing units is often impeded by wax and hydrates deposits; sand erosion and corrosion-related being frequently encountered. During the last decades it was shown that apparently different problems such as sand erosion and deposition-removal of paraffin during turbulent pipe transportation are the effect of near-wall flow transport related to burst-sweep specific turbulent activity. Coherent structures visualized as a sequence of (in) bursts and outburst sweep actions has already been suggested as an important factor for understanding aging of the paraffin deposit and the deposition-removal balance controlling the grow of deposit. This paper, using published models, investigates the effect of near-wall turbulence on removal of small-size particulate matter, first through direct measurement of burst activity, then, using fine sand and glass beads transported as moving bed during turbulent flow condition. Lack of experimental data for assessing the effect of turbulent liquid pipe flow on burst activity for removal of fines created challenging problems. Those include direct assessment of burst frequency and measurement of the rate of fine sand grading and on-line sampling and measuring the rate of fine removal during sand bed or lenticular deposits transportation. Laboratory work uses a Particle Image Velocimetry (PIV) instrument to observe and quantified the burst activity as it progresses from the pipe wall to the turbulent core flow. Experimental data are closely compared to existing literature models; in addition the present laboratory measurements allow for describing the dynamic of a burst as it evolves from the pipe wall to turbulent core regions. The frequency of burst removal is further compared with changing of size distribution during the sand bed-slurry transport stage of this work. Results obtained so far at the University of Alberta with the aid of an experimental loop designed and operated for observing and quantifying selective (size-density) radial-axial transportation of fines are discussed and summarized. It is suggested that the experimental data on fines removal and deposition, particularly related to near-turbulent structure activity, is important for understanding and mitigating a broad range of near-wall turbulent-related flow assurance problems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".